$\epsilon$-shotgun: $\epsilon$-greedy Batch Bayesian Optimisation
- URL: http://arxiv.org/abs/2002.01873v2
- Date: Sun, 29 Mar 2020 15:25:31 GMT
- Title: $\epsilon$-shotgun: $\epsilon$-greedy Batch Bayesian Optimisation
- Authors: George De Ath, Richard M. Everson, Jonathan E. Fieldsend, Alma A. M.
Rahat
- Abstract summary: We present an $epsilon$-greedy procedure for Bayesian optimisation in batch settings.
Our $epsilon$-shotgun algorithm leverages the model's prediction, uncertainty, and the approximated rate of change of the landscape.
We empirically evaluate the $epsilon$-shotgun methods on a range of synthetic functions and two real-world problems.
- Score: 0.029410438275861574
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Bayesian optimisation is a popular, surrogate model-based approach for
optimising expensive black-box functions. Given a surrogate model, the next
location to expensively evaluate is chosen via maximisation of a cheap-to-query
acquisition function. We present an $\epsilon$-greedy procedure for Bayesian
optimisation in batch settings in which the black-box function can be evaluated
multiple times in parallel. Our $\epsilon$-shotgun algorithm leverages the
model's prediction, uncertainty, and the approximated rate of change of the
landscape to determine the spread of batch solutions to be distributed around a
putative location. The initial target location is selected either in an
exploitative fashion on the mean prediction, or -- with probability $\epsilon$
-- from elsewhere in the design space. This results in locations that are more
densely sampled in regions where the function is changing rapidly and in
locations predicted to be good (i.e close to predicted optima), with more
scattered samples in regions where the function is flatter and/or of poorer
quality. We empirically evaluate the $\epsilon$-shotgun methods on a range of
synthetic functions and two real-world problems, finding that they perform at
least as well as state-of-the-art batch methods and in many cases exceed their
performance.
Related papers
- Bayesian Optimization with Conformal Prediction Sets [44.565812181545645]
Conformal prediction is an uncertainty quantification method with coverage guarantees even for misspecified models.
We propose conformal Bayesian optimization, which directs queries towards regions of search space where the model predictions have guaranteed validity.
In many cases we find that query coverage can be significantly improved without harming sample-efficiency.
arXiv Detail & Related papers (2022-10-22T17:01:05Z) - Generalizing Bayesian Optimization with Decision-theoretic Entropies [102.82152945324381]
We consider a generalization of Shannon entropy from work in statistical decision theory.
We first show that special cases of this entropy lead to popular acquisition functions used in BO procedures.
We then show how alternative choices for the loss yield a flexible family of acquisition functions.
arXiv Detail & Related papers (2022-10-04T04:43:58Z) - Batch Bayesian Optimization via Particle Gradient Flows [0.5735035463793008]
We show how to find global optima of objective functions which are only available as a black-box or are expensive to evaluate.
We construct a new function based on multipoint expected probability which is over the space of probability measures.
arXiv Detail & Related papers (2022-09-10T18:10:15Z) - Surrogate modeling for Bayesian optimization beyond a single Gaussian
process [62.294228304646516]
We propose a novel Bayesian surrogate model to balance exploration with exploitation of the search space.
To endow function sampling with scalability, random feature-based kernel approximation is leveraged per GP model.
To further establish convergence of the proposed EGP-TS to the global optimum, analysis is conducted based on the notion of Bayesian regret.
arXiv Detail & Related papers (2022-05-27T16:43:10Z) - Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian
Processes [0.0]
We adapt the Vecchia approximation, a popular GP approximation from spatial statistics, to enable scalable high-dimensional Bayesian optimization.
We focus on the use of our warped Vecchia GP in trust-region Bayesian optimization via Thompson sampling.
arXiv Detail & Related papers (2022-03-02T23:55:14Z) - Local policy search with Bayesian optimization [73.0364959221845]
Reinforcement learning aims to find an optimal policy by interaction with an environment.
Policy gradients for local search are often obtained from random perturbations.
We develop an algorithm utilizing a probabilistic model of the objective function and its gradient.
arXiv Detail & Related papers (2021-06-22T16:07:02Z) - Bayesian Optimistic Optimisation with Exponentially Decaying Regret [58.02542541410322]
The current practical BO algorithms have regret bounds ranging from $mathcalO(fraclogNsqrtN)$ to $mathcal O(e-sqrtN)$, where $N$ is the number of evaluations.
This paper explores the possibility of improving the regret bound in the noiseless setting by intertwining concepts from BO and tree-based optimistic optimisation.
We propose the BOO algorithm, a first practical approach which can achieve an exponential regret bound with order $mathcal O(N-sqrt
arXiv Detail & Related papers (2021-05-10T13:07:44Z) - Bayesian Algorithm Execution: Estimating Computable Properties of
Black-box Functions Using Mutual Information [78.78486761923855]
In many real world problems, we want to infer some property of an expensive black-box function f, given a budget of T function evaluations.
We present a procedure, InfoBAX, that sequentially chooses queries that maximize mutual information with respect to the algorithm's output.
On these problems, InfoBAX uses up to 500 times fewer queries to f than required by the original algorithm.
arXiv Detail & Related papers (2021-04-19T17:22:11Z) - Bayesian Optimization of Risk Measures [7.799648230758491]
We consider Bayesian optimization of objective functions of the form $rho[ F(x, W) ]$, where $F$ is a black-box expensive-to-evaluate function.
We propose a family of novel Bayesian optimization algorithms that exploit the structure of the objective function to substantially improve sampling efficiency.
arXiv Detail & Related papers (2020-07-10T18:20:46Z) - Incorporating Expert Prior in Bayesian Optimisation via Space Warping [54.412024556499254]
In big search spaces the algorithm goes through several low function value regions before reaching the optimum of the function.
One approach to subside this cold start phase is to use prior knowledge that can accelerate the optimisation.
In this paper, we represent the prior knowledge about the function optimum through a prior distribution.
The prior distribution is then used to warp the search space in such a way that space gets expanded around the high probability region of function optimum and shrinks around low probability region of optimum.
arXiv Detail & Related papers (2020-03-27T06:18:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.